位于上海市的 20 个优质product architect职位（正在招聘！）

The market is beginning to understand what leading technology companies have demonstrated: AI products fail not because the models are weak but because deployment is broken. The gap between a successful AI pilot and an AI capability that scales is bridged by engineers who can translate platform capability into measurable business value inside a real enterprise environment. That is this role.

Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.

THE WORK

Lead enterprise AI platform deployments across complex multi-stakeholder client environments — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — owning the full programme from architecture through adoption
Own programme-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics attached
Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments
Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi-system workflow integration at programme scale
Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritization frameworks, and multi-year AI adoption roadmaps
Define and publish reusable reinvention blueprints, patterns, and accelerators that scale across multiple client engagements and grow the FDE practice
Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams
Codify delivery learnings, failure patterns, and engineering standards that shape the FDE practice and enable the next generation of forward deployed engineers

QUALIFICATION

8-10 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Minimum of 7 years of experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
8-10 years of experience leading software engineering teams: overseeing delivery, allocating resources across workstreams, and owning the professional development of direct reports"
Demonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify
Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on
Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level
Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching
People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations

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